Data engineering on Google Cloud Platform

g-company is committed to helping organizations transform and become more flexible. We believe in the power of Google Cloud and Salesforce and developed a set of solutions that can be implemented independently, at your pace and in a sequence that fits your path to a modern architecture.

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This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on GCP.

Through a combination of presentations, demos, and hand-on labs, you will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.<h3>Course overview</h3>What you'll learn:This course teaches participants the following skills:<ul><li>Design and build data processing systems on Google Cloud Platform</li><li>Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow</li><li>Derive business insights from extremely large datasets using Google BigQuery</li><li>Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML</li><li>Leverage unstructured data using Spark and ML APIs on Cloud Dataproc</li><li>Enable instant insights from streaming data</li></ul>Prerequisites:<ul><li>Basic proficiency with common query language such as SQL</li><li>Experience with data modeling, extract, transform, load activities</li><li>Developing applications using a common programming language such Python</li><li>Familiarity with Machine Learning and/or statistics</li></ul><h3>Content</h3>The course includes presentations, demonstrations, and hands-on labs.Module 1: Introduction to Data Engineering<ul><li>Explore the role of a data engineer.</li><li>Analyze data engineering challenges.</li><li>Intro to BigQuery.</li><li>Data Lakes and Data Warehouses.</li><li>Demo: Federated Queries with BigQuery.</li><li>Transactional Databases vs Data Warehouses.</li><li>Website Demo: Finding PII in your dataset with DLP API.</li><li>Partner effectively with other data teams.</li><li>Manage data access and governance.</li><li>Build production-ready pipelines.</li><li>Review GCP customer case study.</li><li>Lab: Analyzing Data with BigQuery.</li></ul>Module 2: Building a Data Lake<ul><li>Introduction to Data Lakes.</li><li>Data Storage and ETL options on GCP.</li><li>Building a Data Lake using Cloud Storage.</li><li>Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.</li><li>Securing Cloud Storage.</li><li>Storing All Sorts of Data Types.</li><li>Video Demo: Running federated queries on Parquet and ORC files in BigQuery.</li><li>Cloud SQL as a relational Data Lake.</li><li>Lab: Loading Taxi Data into Cloud SQL.</li></ul>Module 3: Building a Data Warehouse<ul><li>The modern data warehouse.</li><li>Intro to BigQuery.</li><li>Demo: Query TB+ of data in seconds.</li><li>Getting Started.</li><li>Loading Data.</li><li>Video Demo: Querying Cloud SQL from BigQuery.</li><li>Lab: Loading Data into BigQuery.</li><li>Exploring Schemas.</li><li>Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.</li><li>Schema Design.</li><li>Nested and Repeated Fields.</li><li>Demo: Nested and repeated fields in BigQuery.</li><li>Lab: Working with JSON and Array data in BigQuery.</li><li>Optimizing with Partitioning and Clustering.</li><li>Demo: Partitioned and Clustered Tables in BigQuery.</li><li>Preview: Transforming Batch and Streaming Data.</li></ul>Module 4: Introduction to Building Batch Data Pipelines,<ul><li>EL, ELT, ETL.</li><li>Quality considerations.</li><li>How to carry out operations in BigQuery.</li><li>Demo: ELT to improve data quality in BigQuery.</li><li>Shortcomings.</li><li>ETL to solve data quality issues.</li></ul>Module 5: Executing Spark on Cloud Dataproc<ul><li>The Hadoop ecosystem.</li><li>Running Hadoop on Cloud Dataproc.</li><li>GCS instead of HDFS.</li><li>Optimizing Dataproc.</li><li>Lab: Running Apache Spark jobs on Cloud Dataproc.</li></ul>Module 6: Serverless Data Processing with Cloud Dataflow<ul><li>Cloud Dataflow.</li><li>Why customers value Dataflow.</li><li>Dataflow Pipelines.</li><li>Lab: A Simple Dataflow Pipeline (Python/Java).</li><li>Lab: MapReduce in Dataflow (Python/Java).</li><li>Lab: Side Inputs (Python/Java).</li><li>Dataflow Templates.</li><li>Dataflow SQL.</li></ul>Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer<ul><li>Building Batch Data Pipelines visually with Cloud Data Fusion.</li><li>Components.</li><li>UI Overview.</li><li>Building a Pipeline.</li><li>Exploring Data using Wrangler.</li><li>Lab: Building and executing a pipeline graph in Cloud Data Fusion.</li><li>Orchestrating work between GCP services with Cloud Composer.</li><li>Apache Airflow Environment.</li><li>DAGs and Operators.</li><li>Workflow Scheduling.</li><li>Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.</li><li>Monitoring and Logging.</li><li>Lab: An Introduction to Cloud Composer.</li></ul>Module 8: Introduction to Processing Streaming Data<ul><li>Processing Streaming Data.</li></ul>Module 9: Serverless Messaging with Cloud Pub/Sub<ul><li>Cloud Pub/Sub.</li><li>Lab: Publish Streaming Data into Pub/Sub.</li></ul>Module 10: Cloud Dataflow Streaming Features<ul><li>Cloud Dataflow Streaming Features.</li><li>Lab: Streaming Data Pipelines.</li></ul>Module 11: High-Throughput BigQuery and Bigtable Streaming Features<ul><li>BigQuery Streaming Features.</li><li>Lab: Streaming Analytics and Dashboards.</li><li>Cloud Bigtable.</li><li>Lab: Streaming Data Pipelines into Bigtable.</li></ul>Module 12: Advanced BigQuery Functionality and Performance<ul><li>Analytic Window Functions.</li><li>Using With Clauses.</li><li>GIS Functions.</li><li>Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.</li><li>Performance Considerations.</li><li>Lab: Optimizing your BigQuery Queries for Performance.</li><li>Optional Lab: Creating Date-Partitioned Tables in BigQuery.</li></ul>Module 13: Introduction to Analytics and AI<ul><li>What is AI?.</li><li>From Ad-hoc Data Analysis to Data Driven Decisions.</li><li>Options for ML models on GCP.</li></ul>Module 14: Prebuilt ML model APIs for Unstructured Data<ul><li>Unstructured Data is Hard.</li><li>ML APIs for Enriching Data.</li><li>Lab: Using the Natural Language API to Classify Unstructured Text.</li></ul>Module 15: Big Data Analytics with Cloud AI Platform Notebooks<ul><li>Whats a Notebook.</li><li>BigQuery Magic and Ties to Pandas.</li><li>Lab: BigQuery in Jupyter Labs on AI Platform.</li></ul>Module 16: Production ML Pipelines with Kubeflow<ul><li>Ways to do ML on GCP.</li><li>Kubeflow.</li><li>AI Hub.</li><li>Lab: Running AI models on Kubeflow.</li></ul>Module 17: Custom Model building with SQL in BigQuery ML<ul><li>BigQuery ML for Quick Model Building.</li><li>Demo: Train a model with BigQuery ML to predict NYC taxi fares.</li><li>Supported Models.</li><li>Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.</li><li>Lab Option 2: Movie Recommendations in BigQuery ML.</li></ul>Module 18: Custom Model building with Cloud AutoML<ul><li>Why Auto ML?</li><li>Auto ML Vision.</li><li>Auto ML NLP.</li><li>Auto ML Tables.</li></ul>

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Data engineering on Google Cloud Platform

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